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Measurement Methods and Technologies for Indoor Assisted Living

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Internet of Things".

Deadline for manuscript submissions: 15 February 2027 | Viewed by 861

Editors


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Guest Editor
Department of Information Engineering, Marche Polytechnic University, Ancona, Italy
Interests: data acquisition systems; compressed sensing; IoT; measurement; signal processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Roma, Italy
Interests: design, development, and validation of wearable systems and algorithms for monitoring physiological parameters in clinical, occupational, and sports settings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of measurement systems for indoor assisted living has become a cornerstone for enhancing the autonomy, safety and well-being of individuals, particularly within aging populations and those with disabilities. This Special Issue seeks to explore the latest advancements in sensor fusion, artificial intelligence (AI) and innovative measurement methodologies that enable real-time health and environmental monitoring in ambient assisted living environments. Key topics provide critical support for individuals with cognitive and physical impairments. This Special Issue addresses the application of machine learning and semantic segmentation techniques for indoor localization and obstacle classification, facilitating enhanced mobility and navigation within home environments, thereby reducing the risk of accidents and improving overall safety. Furthermore, the role of tele-rehabilitation and tele-monitoring systems in supporting the continuum of care represent a central theme, with particular emphasis on how these technologies enable remote health assessments, personalized interventions and continuous patient monitoring. The Issue provides a platform for discussing the challenges and opportunities associated with the integration of these technologies into comprehensive, user-centric solutions, by reflecting recent developments in measurement systems for aging, disability support and home-based care. By fostering interdisciplinary dialog, the Special Issue aims to highlight the potential of measurement systems and AI-driven innovations to improve the quality of life and rehabilitation outcomes for individuals in indoor assisted living settings.

Topics of interest include, but are not restricted to, the following:

  • Measurement methods in medical environment
  • Sensor fusion and AI-integrated solutions for ambient assisted living
  • Signal processing for physiological and health monitoring
  • Machine learning and semantic segmentation for indoor localization and obstacle classification
  • Tele-rehabilitation and tele-monitoring for continuum of care
  • Privacy-preserving and unobtrusive sensing
  • Wearable and contactless monitoring
  • Activity recognition
  • Human–robot interaction
  • Clinical validation

Dr. Grazia Iadarola
Dr. Chiara Romano
Guest Editors

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Keywords

  • indoor assisted living
  • sensor fusion
  • artificial intelligence
  • health and environmental monitoring
  • tele-rehabilitation and tele-monitoring

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Published Papers (1 paper)

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Research

25 pages, 949 KB  
Article
A Method for Optimized Monitoring of Indoor Air Quality in Public Buildings
by Filippo Ruffa, Grazia Iadarola, Alberto De Capua and Claudio De Capua
Sensors 2026, 26(14), 4559; https://doi.org/10.3390/s26144559 - 18 Jul 2026
Viewed by 591
Abstract
A huge effort has been directed towards research and development of new measurement systems for maximizing comfort and safety in public buildings by monitoring indoor air quality (IAQ). In fact, according to World Health Organization, exposure to chemical, biological, and physical agents in [...] Read more.
A huge effort has been directed towards research and development of new measurement systems for maximizing comfort and safety in public buildings by monitoring indoor air quality (IAQ). In fact, according to World Health Organization, exposure to chemical, biological, and physical agents in poorly ventilated spaces can lead to psycho-physical discomfort as well as respiratory and neurological diseases. Recent advances in the Internet of Things (IoT) have paved the ground for the design and implementation of distributed measurement systems with higher sensor density and computational capacity. While these systems provide accurate assessments of individual rooms, they do not account for personal exposure to varying air quality levels over time. In public buildings such as schools, universities, and workplaces, occupants frequently move between rooms according to predefined schedules, resulting in heterogeneous exposure patterns. To address this issue, this paper proposes an innovative IAQ measurement technique for public buildings, shifting the focus from room-based assessment to occupant-centered assessment. Unlike wearable or portable personal monitors, the proposed technique infers occupant location from the institutional timetable and combines it with the fixed sensor infrastructure already installed in the rooms, requiring no additional devices to be worn. Individual conditions are quantified through a new personalized metric that integrates instantaneous air quality, cumulative individual exposure over time, and thermal comfort into a single index that is evaluated against occupant-specific thresholds. The technique is validated using real-world data, demonstrating higher potential to ensure safety and comfort compared to the state of the art. Full article
(This article belongs to the Special Issue Measurement Methods and Technologies for Indoor Assisted Living)
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